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20122023
most citedPoisoning Language Models During Instruction Tuning

38 citations · 53 across the 8 of their papers we have counts for

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Showing cs.CLShow all

7 papers · 1 filter

cs.CL2023

Incorporating Worker Perspectives into MTurk Annotation Practices for NLP

Olivia Huang, Eve Fleisig, Dan Klein

Current practices regarding data collection for natural language processing on Amazon Mechanical Turk (MTurk) often rely on a combination of studies on data quality and heuristics…

cs.CL2023

Improving Pacing in Long-Form Story Planning

Yichen Wang, Kevin Yang, Xiaoming Liu +1

Existing LLM-based systems for writing long-form stories or story outlines frequently suffer from unnatural pacing, whether glossing over important events or over-elaborating on in…

cs.CL2023

Modular Visual Question Answering via Code Generation

Sanjay Subramanian, Medhini Narasimhan, Kushal Khangaonkar +6

We present a framework that formulates visual question answering as modular code generation. In contrast to prior work on modular approaches to VQA, our approach requires no additi…

cs.CL2023

Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents

Catherine Chen, Zejiang Shen, Dan Klein +3

Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers. Layout-infused LMs are ofte…

cs.CL20231 cited

Decomposing Complex Queries for Tip-of-the-tongue Retrieval

Kevin Lin, Kyle Lo, Joseph E. Gonzalez +1

When re-finding items, users who forget or are uncertain about identifying details often rely on creative strategies for expressing their information needs -- complex queries that…

cs.CL202338 cited

Poisoning Language Models During Instruction Tuning

Alexander Wan, Eric Wallace, Sheng Shen +1

Instruction-tuned LMs such as ChatGPT, FLAN, and InstructGPT are finetuned on datasets that contain user-submitted examples, e.g., FLAN aggregates numerous open-source datasets and…